Recently, worldwide interest in alternative fuel (e.g., battery-driven, plug-in hybrid, etc.) vehicles has increased considerably. Carbon dioxide emissions from internal combustion engines are widely considered a key factor in global warming. In general, alternative fuel vehicles are seen as much safer on the environment due to their emission-free operation.
One significant drawback for battery-based alternative fuel vehicles, including battery-driven and plug-in hybrid vehicles, is that it is very difficult to estimate or measure the remaining life of the battery. Current estimation techniques and algorithms only observe the battery as a whole. Such techniques cannot detect the difference between a good subset of batteries and a bad or even broken subset of batteries within a given stack. Even worse, if a single battery within the stack dies, this can cause deterioration of the entire stack since a single dead battery can in effect shut off an entire subset of batteries connected in series. This can in turn result in a higher currency demand for the remaining subset of batteries connected in parallel. When individual battery cells are driven outside their normal operation parameters, their lifespans can be significantly shortened, thereby making any State of Charge (SOC), State of Health (SOH) and/or Remaining Useful Life (RUL) estimations of the overall battery system unacceptably inaccurate.
In order to provide an accurate estimate of SOC, SOH or RUL, it would be necessary to know the performance characteristics, such as voltage and temperature, of each individual cell comprising the overall battery stack. However, heretofore this has been technically and economically infeasible since the large battery stacks used to power alternative fuel vehicles can consist of thousands of individual connected cells. Accordingly, there is a need in the art for a system and apparatus for monitoring the performance of individual cells within large battery stacks.